A Reuse-based framework for the design of analog and mixed-signal ICs

Despite the spectacular breakthroughs of the semiconductor industry, the ability to design integrated circuits (ICs) under stringent time-to-market (TTM) requirements is lagging behind integration capacity, so far keeping pace with still valid Moore's Law. The resulting gap is threatening with slowing down such a phenomenal growth. The design community believes that it is only by means of powerful CAD tools and design methodologies -and, possibly, a design paradigm shift-that this design gap can be bridged. In this sense, reuse-based design is seen as a promising solution, and concepts such as IP Block, Virtual Component, and Design Reuse have become commonplace thanks to the significant advances in the digital arena. Unfortunately, the very nature of analog and mixed-signal (AMS) design has hindered a similar level of consensus and development. This paper presents a framework for the reuse-based design of AMS circuits. The framework is founded on three key elements: (1) a CAD-supported hierarchical design flow that facilitates the incorporation of AMS reusable blocks, reduces the overall design time, and expedites the management of increasing AMS design complexity; (2) a complete, clear definition of the AMS reusable block, structured into three separate facets or views: the behavioral, structural, and layout facets, the two first for top-down electrical synthesis and bottom-up verification, the latter used during bottom-up physical synthesis; (3) the design for reusability set of tools, methods, and guidelines that, relying on intensive parameterization as well as on design knowledge capture and encapsulation, allows to produce fully reusable AMS blocks. A case study and a functional silicon prototype demonstrate the validity of the paper's proposals.Ministerio de Educación y Ciencia TEC2004-0175

Learning Approaches to Analog and Mixed Signal Verification and Analysis

The increased integration and interaction of analog and digital components within a system has amplified the need for a fast, automated, combined analog, and digital verification methodology. There are many automated characterization, test, and verification methods used in practice for digital circuits, but analog and mixed signal circuits suffer from long simulation times brought on by transistor-level analysis. Due to the substantial amount of simulations required to properly characterize and verify an analog circuit, many undetected issues manifest themselves in the manufactured chips. Creating behavioral models, a circuit abstraction of analog components assists in reducing simulation time which allows for faster exploration of the design space. Traditionally, creating behavioral models for non-linear circuits is a manual process which relies heavily on design knowledge for proper parameter extraction and circuit abstraction. Manual modeling requires a high level of circuit knowledge and often fails to capture critical effects stemming from block interactions and second order device effects. For this reason, it is of interest to extract the models directly from the SPICE level descriptions so that these effects and interactions can be properly captured. As the devices are scaled, process variations have a more profound effect on the circuit behaviors and performances. Creating behavior models from the SPICE level descriptions, which include input parameters and a large process variation space, is a non-trivial task. In this dissertation, we focus on addressing various problems related to the design automation of analog and mixed signal circuits. Analog circuits are typically highly specialized and fined tuned to fit the desired specifications for any given system reducing the reusability of circuits from design to design. This hinders the advancement of automating various aspects of analog design, test, and layout. At the core of many automation techniques, simulations, or data collection are required. Unfortunately, for some complex analog circuits, a single simulation may take many days. This prohibits performing any type of behavior characterization or verification of the circuit. This leads us to the first fundamental problem with the automation of analog devices. How can we reduce the simulation cost while maintaining the robustness of transistor level simulations? As analog circuits can vary vastly from one design to the next and are hardly ever comprised of standard library based building blocks, the second fundamental question is how to create automated processes that are general enough to be applied to all or most circuit types? Finally, what circuit characteristics can we utilize to enhance the automation procedures? The objective of this dissertation is to explore these questions and provide suitable evidence that they can be answered. We begin by exploring machine learning techniques to model the design space using minimal simulation effort. Circuit partitioning is employed to reduce the complexity of the machine learning algorithms. Using the same partitioning algorithm we further explore the behavior characterization of analog circuits undergoing process variation. The circuit partitioning is general enough to be used by any CMOS based analog circuit. The ideas and learning gained from behavioral modeling during behavior characterization are used to improve the simulation through event propagation, input space search, complexity and information measurements. The reduction of the input space and behavioral modeling of low complexity, low information primitive elements reduces the simulation time of large analog and mixed signal circuits by 50-75%. The method is extended and applied to assist in analyzing analog circuit layout. All of the proposed methods are implemented on analog circuits ranging from small benchmark circuits to large, highly complex and specialized circuits. The proposed dependency based partitioning of large analog circuits in the time domain allows for fast identification of highly sensitive transistors as well as provides a natural division of circuit components. Modeling analog circuits in the time domain with this partitioning technique and SVM learning algorithms allows for very fast transient behavior predictions, three orders of magnitude faster than traditional simulators, while maintaining 95% accuracy. Analog verification can be explored through a reduction of simulation time by utilizing the partitions, information and complexity measures, and input space reduction. Behavioral models are created using supervised learning techniques for detected primitive elements. We will show the effectiveness of the method on four analog circuits where the simulation time is decreased by 55-75%. Utilizing the reduced simulation method, critical nodes can be found quickly and efficiently. The nodes found using this method match those found by an experienced layout engineer, but are detected automatically given the design and input specifications. The technique is further extended to find the tolerance of transistors to both process variation and power supply fluctuation. This information allows for corrections in layout overdesign or guidance in placing noise reducing components such as guard rings or decoupling capacitors. The proposed approaches significantly reduce the simulation time required to perform the tasks traditionally, maintain high accuracy, and can be automated

Characterization and Verification Environment for the RD53A Pixel Readout Chip in 65 nm CMOS

The RD53 collaboration is currently designing a large scale prototype pixel readout chip in 65 nm CMOS technology for the phase 2 upgrades at the HL-LHC. The RD53A chip will be available by the end of the year 2017 and will be extensively tested to confirm if the circuit and the architecture make a solid foundation for the final pixel readout chips for the experiments at the HL-LHC. A test and data acquisition system for the RD53A chip is currently under development to perform single-chip and multi-chip module measurements. In addition, the verification of the RD53A design is performed in a dedicated simulation environment. The concept and the implementation of the test and data acquisition system and the simulation environment, which are based on a modular data acquisition and system testing framework, are presented in this work

A design tool for high-resolution high-frequency cascade continuous- time Σ∆ modulators

Event: Microtechnologies for the New Millennium, 2007, Maspalomas, Gran Canaria, SpainThis paper introduces a CAD methodology to assist the de signer in the implementation of continuous-time (CT) cas- cade Σ∆ modulators. The salient features of this methodology ar e: (a) flexible behavioral modeling for optimum accuracy- efficiency trade-offs at different stages of the top-down synthesis process; (b) direct synthesis in the continuous-time domain for minimum circuit complexity and sensitivity; a nd (c) mixed knowledge-based and optimization-based architec- tural exploration and specification transmission for enhanced circuit performance. The applicability of this methodology will be illustrated via the design of a 12 bit 20 MHz CT Σ∆ modulator in a 1.2V 130nm CMOS technology.Ministerio de Ciencia y Educación TEC2004-01752/MICMinisterio de Industria, Turismo y Comercio FIT-330100-2006-134 SPIRIT Projec

Simulation-based high-level synthesis of Nyquist-rate data converters using MATLAB/SIMULINK

This paper presents a toolbox for the simulation, optimization and high-level synthesis of Nyquist-rate Analog-to-Digital (A/D) and Digital-to-Analog (D/A) Converters in MATLAB®. The embedded simulator uses SIMULINK® C-coded S-functions to model all required subcircuits including their main error mechanisms. This approach allows to drastically speed up the simulation CPU-time up to 2 orders of magnitude as compared with previous approaches - based on the use of SIMULINK® elementary blocks. Moreover, S-functions are more suitable for implementing a more detailed description of the circuit. For all subcircuits, the accuracy of the behavioral models has been verified by electrical simulation using HSPICE. For synthesis purposes, the simulator is used for performance evaluation and combined with an hybrid optimizer for design parameter selection. The optimizer combines adaptive statistical optimization algorithm inspired in simulated annealing with a design-oriented formulation of the cost function. It has been integrated in the MATLAB/SIMULINK® platform by using the MATLAB® engine library, so that the optimization core runs in background while MATLAB® acts as a computation engine. The implementation on the MATLAB® platform brings numerous advantages in terms of signal processing, high flexibility for tool expansion and simulation with other electronic subsystems. Additionally, the presented toolbox comprises a friendly graphical user interface to allow the designer to browse through all steps of the simulation, synthesis and post-processing of results. In order to illustrate the capabilities of the toolbox, a 0.13)im CMOS 12bit@80MS/s analog front-end for broadband power line communications, made up of a pipeline ADC and a current steering DAC, is synthesized and high-level sized. Different experiments show the effectiveness of the proposed methodology.Ministerio de Ciencia y Tecnología TIC2003-02355RAICONI

The RD53 Collaboration's SystemVerilog-UVM Simulation Framework and its General Applicability to Design of Advanced Pixel Readout Chips

The foreseen Phase 2 pixel upgrades at the LHC have very challenging requirements for the design of hybrid pixel readout chips. A versatile pixel simulation platform is as an essential development tool for the design, verification and optimization of both the system architecture and the pixel chip building blocks (Intellectual Properties, IPs). This work is focused on the implemented simulation and verification environment named VEPIX53, built using the SystemVerilog language and the Universal Verification Methodology (UVM) class library in the framework of the RD53 Collaboration. The environment supports pixel chips at different levels of description: its reusable components feature the generation of different classes of parameterized input hits to the pixel matrix, monitoring of pixel chip inputs and outputs, conformity checks between predicted and actual outputs and collection of statistics on system performance. The environment has been tested performing a study of shared architectures of the trigger latency buffering section of pixel chips. A fully shared architecture and a distributed one have been described at behavioral level and simulated; the resulting memory occupancy statistics and hit loss rates have subsequently been compared.Comment: 15 pages, 10 figures (11 figure files), submitted to Journal of Instrumentatio

Experimental study of artificial neural networks using a digital memristor simulator

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a fully digital implementation of a memristor hardware simulator, as the core of an emulator, based on a behavioral model of voltage-controlled threshold-type bipolar memristors. Compared to other analog solutions, the proposed digital design is compact, easily reconfigurable, demonstrates very good matching with the mathematical model on which it is based, and complies with all the required features for memristor emulators. We validated its functionality using Altera Quartus II and ModelSim tools targeting low-cost yet powerful field programmable gate array (FPGA) families. We tested its suitability for complex memristive circuits as well as its synapse functioning in artificial neural networks (ANNs), implementing examples of associative memory and unsupervised learning of spatio-temporal correlations in parallel input streams using a simplified STDP. We provide the full circuit schematics of all our digital circuit designs and comment on the required hardware resources and their scaling trends, thus presenting a design framework for applications based on our hardware simulator.Peer ReviewedPostprint (author's final draft